See What You Feel: Visualizing Static Haptic Scenes
Bibliographic record
Abstract
Novices to haptic design may struggle to predict how different force-feedback effects would combine across the workspace of their device. While haptic simulations often include a visual component, these graphics frequently rely on metaphor. For example, while a graphical display of a piece of wood may be used to indicate that a surface should feel similar to wood, this would not necessarily reflect the actual forcefeedback effects one would experience when interacting with the simulated material. As more haptic elements and interactions are included, a novice would need to feel the resulting forces sequentially and may be surprised when these results clash with expectations formed from the metaphorical view. We aim to develop visualizations of haptic properties from two-dimensional scenes to help novices develop an intuition for how the components of their designs interact. We present our current approaches and discuss planned work to integrate our method into beginner-friendly development environments so that visualizations are easily accessible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".